Commercial, residential and industrial facilities are becoming more versatile in managing their energy needs. Traditionally, nearly all energy consumers purchased power from a regulated utility, with few maintaining on-site generation for emergency backup. Now, some facilities are using on-site generation as primary power and other facilities are even selling locally-generated power back to the grid. Part of the reason for the change can be attributed to deregulation of the energy industry and the now widespread availability of distributed generation equipment.
Distributed generation equipment (DGE) refers to one of various power generation technologies which can be used on-site at a facility for various purposes, such as powering electrical equipment and heating and cooling systems, for example. Some DGE generate excess heat, which can be used in building or industrial processes, such as heating domestic hot water. A facility using DGE can be as large as an industrial complex or as small as a private residence. DGE is typically used alongside or in place of standard “grid” power provided by a utility or private enterprise.
DGE technologies include microturbines, fuel cells, internal and external combustion engines, reciprocating engines, photovoltaic cells, microgrids and other generation and storage types. DGE typically supplies both electrical energy and heat. Some DGE use essentially free fuels like sunlight or landfill gas while others use natural gas, propane or hydrogen. The fuel type can help determine operational parameters for DGE. For example, free fuel burning DGE might be operated at maximum power level whenever the fuel is available and the load can use the power, while DGE using other fuel types may only be run when it is most economical. Of course, specific circumstances may dictate different operating conditions regardless of fuel type.
Determining when to operate each DGE within a distributed generation environment and at what power level can be a rather complex decision, affected by the cost of fuel, the cost of the electric power or heat that is deferred, and, in some cases, the impact of the emissions both from the DGE device and from the central generator, for example. Such determination must also take into account that the demands for electric power and heat may not occur at the same time. In addition, there may be other services that the DGE can provide, such as, for example, voltage regulation, standby power, and ride through for voltage sags from the utility. In optimizing the economics of a given installation, operational decisions are directed not only by local power needs, heat and back up power supply, but also by the opportunities provided by the open market.
Various intelligent or expert system designs can be employed to assist in solving power system management problems. An expert system is an artificial intelligence application that uses a knowledge base of human expertise to aid in solving problems. It can use a software program as an interface with the user and then use the data in the knowledge base to process the results. Such intelligent systems can assist in a variety of power system applications, such as economic load dispatch, optimization and loss reduction, fault detection and diagnosis, load forecasting, power system planning, control and analysis, and even security assessment. Some of these applications influence others. For example, electrical load forecasting is very important for power system operators and planners, since many important functions in power system operational planning, such as unit commitment, economic dispatch, maintenance scheduling, and expansion planning are usually performed based on the forecasted loads.
Economic load dispatch, optimization and loss reduction involves managing the operation (dispatching) of generation and transmission facilities to produce the most cost-effective result. Economic dispatch most commonly involves the selection of the lowest-cost available generating units or fuels for powering available units. Fault detection and diagnosis involves managing the system to detect faults more quickly so as to properly restore operation as soon as possible.
Traditional intelligent systems for determining load forecasting have involved off-line processing of vast amounts of data using standard linear regression or neural network modeling. The resulting forecasting models are then used in real-time, focusing largely on aggregate loads and not site-specific loads. Such methods suffer from several disadvantages, including the inability to adapt the forecasting model to changing operational conditions in real time, especially on a site-specific basis.
Beyond neural networks, other expert systems can be employed to assist in solving power management problems, including genetic or evolutionary algorithms, and fuzzy logic systems. Fuzzy logic is a branch of logic based on approximate reasoning. Fuzzy logic allows the use of labels like “slightly,” “moderately,” and “very,” so that statements may be made with varying degrees of precision. This flexibility is useful in coping with the imprecision of real-world situations, such as the management of distributed generation technologies.